Executive Summary
The electronic business environment has given rise to new forms of advertising instruments, such as banner advertising, pop-ups, and search-based links. However, little research exists on the relationship between advertising exposure and actual purchasing on the Internet. In this research, the authors focus on a previously unexplored question: Does banner advertising affect purchasing patterns on the Internet? In particular, using a behavioral database that consists of customer purchases at a Web site along with individual advertising exposure, the authors measure the impact of banner advertising on current customers’ probabilities of repurchase while accounting for duration dependence.
The authors formulate a model of individual purchase timing behavior as a function of advertising exposure. They model the probability of a current customer making a purchase in any given week (since the prior purchase) with a survival model. The duration dependence in the customers’ purchase behavior is captured through a flexible, piecewise exponential hazard function. The advertising covariates enter through a proportional hazards specification. These covariates, richer than those that have typically been used in prior research, consist of strictly advertising variables, such as weight and quality, as well as advertising/individual browsing variables, represented by how many and which pages expose customers to advertising. The model is cast in a hierarchical Bayesian framework, thus enabling the authors to control for unobserved individual differences in the advertising response parameters and to investigate the impact of targeted advertising strategies.
The results show that the number of exposures, number of Web sites, and number of pages on which a customer is exposed to advertising all have a positive effect on repeat purchase probabilities. Notably, increasing the number of unique creatives to which a customer is exposed lowers this probability. The unobserved heterogeneity in the response parameters shows that the returns from targeting individual customers are likely to be the highest for the number of advertising exposures, followed by the number of sites on which they are exposed to advertising. To demonstrate the value of the obtained individual response parameters, the authors conduct a simple experiment in which they compare sales response with and without targeting. They show that compared with no targeting, there are increased revenues and profits with targeting. Finally, in terms of the broader area of research on the effects of (any type of) advertising, the authors provide somewhat unique evidence that advertising indeed affects the purchase behavior of current (versus new) customers.
Biography
Puneet Manchanda is Associate Professor of Marketing in the Graduate School of Business at the University of Chicago. He received his MPhil, and PhD in Marketing from Columbia University. His research interests are in micromarketing and targeting, multicategory choice, consumer learning models, individual-level adoption models, and the role of advertising. He uses data from various domains, such as pharmaceuticals, retailing, direct marketing, and Internet marketing, in his research. His modeling interests are in econometric modeling, focusing on Bayesian methodology. His research has appeared in Quantitative Marketing and Economics, Marketing Science, Journal of Marketing Research, Journal of Consumer Psychology, and Marketing Letters. He is on the editorial board of Journal of Marketing Research and Review of Marketing Science.
Jean-Pierre Dubé is Associate Professor of Marketing in the Graduate School of Business at University of Chicago. He received his BSc in Economics from the University of Toronto and his MA and PhD in Economics from Northwestern University. His research applies economic models of empirical industrial organization to study marketing problems. His current areas of research include dynamic oligopoly, price discrimination, competitive advertising, the endogenous formation of market structure, and Internet marketing.
Khim Yong Goh is a doctoral candidate of Marketing in the Graduate School of Business at the University of Chicago. He holds a BSc (first-class honors) in Computer and Information Sciences and an MSc in Information Systems from the National University of Singapore. Before embarking on his doctoral study, he was an information technology consultant and a research scholar in the university. His research interests include consumer purchase behavior models, empirical industrial organization models, high-tech product marketing, the Internet, and direct marketing. He has published in Management Science and has presented papers at the Marketing Science Conference, Albert Haring Symposium, International Conference on Information Systems, and Annual Workshop on Information Technology and Systems.
Pradeep K. Chintagunta is Robert Law Professor of Marketing and Director of the doctoral program in the Graduate School of Business at the University of Chicago. He is interested in studying strategic interactions among firms in vertical and horizontal relationships. His research also includes measuring the effectiveness of marketing activities in pharmaceutical markets, investigating aspects of technology product markets, and analyzing household purchase behavior.
J Marketing Research, Volume 43, Number 1, February 2006
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